Multi-institutional Assessment of Otitis Media Epidemiology Using Real-world Data.

OBJECTIVES: To determine rates and risk factors of pediatric otitis media (OM) using real-world electronic health record (PEDSnet) data from January 2009 through May 2021. STUDY DESIGN: Retrospective cohort study. SETTING: Seven pediatric academic health systems that participate in PEDSnet. METHODS: Children <6 months-old at time of first outpatient, Emergency Department, or inpatient visit were included and followed longitudinally. A time-to-event analysis was performed using a Cox proportional hazards model to estimate hazard ratios for OM incidence based on sociodemographic factors and specific health conditions. RESULTS: The PEDSnet cohort included 910,265 children, 54.3% male, mean age (months) 1.3 [standard deviation (SD) 1.6] and mean follow up (years) 4.3 (SD 3.2). By age 3 years, 39.6% of children had evidence of one OM episode. OM rates decreased following pneumococcal-13 vaccination (PCV-13) and the COVID-19 pandemic. Along with young age, non-Hispanic Black/African American or Hispanic race/ethnicity, public insurance, higher family income, and male sex had higher incidence rates. Health conditions that increased OM risk included cleft palate [adjusted hazard ratio (aHR) 4.0 [95% confidence interval (CI) 3.9-4.2], primary ciliary dyskinesia [aHR 2.5 (95% CI 1.8-3.5)], trisomy 21 [aHR 2.0 (95% CI 1.9-2.1)], atopic dermatitis [aHR 1.4 (95% CI 1.4-1.4)], and gastroesophageal reflux [aHR1.5 (95% CI 1.5-1.5)]. CONCLUSIONS: Approximately 20% of children by age 1 and 40% of children by age 3 years will have experienced an OM episode. OM rates decreased after PCV-13 and COVID-19. Children with abnormal ciliary function or craniofacial conditions, specifically cleft palate, carry the highest risk of OM.

Retrospective Multicenter Cohort Study on Safety and Electroencephalographic Response to Lacosamide for Neonatal Seizures.

BACKGROUND: There is growing evidence supporting the safety and effectiveness of lacosamide in older children. However, minimal data are available for neonates. We aimed to determine the incidence of adverse events associated with lacosamide use and explore the electroencephalographic seizure response to lacosamide in neonates. METHODS: A retrospective cohort study was conducted using data from seven pediatric hospitals from January 2009 to February 2020. For safety outcomes, neonates were followed for ≤30 days from index date. Electroencephalographic response of lacosamide was evaluated based on electroencephalographic reports for ≤3 days. RESULTS: Among 47 neonates, 98% received the first lacosamide dose in the intensive care units. During the median follow-up of 12 days, 19% of neonates died, and the crude incidence rate per 1000 patient-days (95% confidence interval) of the adverse events by diagnostic categories ranged from 2.8 (0.3, 10.2) for blood or lymphatic system disorders and nervous system disorders to 10.5 (4.2, 21.6) for cardiac disorders. Electroencephalographic seizures were observed in 31 of 34 patients with available electroencephalographic data on the index date. There was seizure improvement in 29% of neonates on day 1 and also in 29% of neonates on day 2. On day 3, there was no change in 50% of neonates and unknown change in 50% of neonates. CONCLUSIONS: The results are reassuring regarding the safety of lacosamide in neonates. Although some neonates had fewer seizures after lacosamide administration, the lack of a comparator arm and reliance on qualitative statements in electroencephalographic reports limit the preliminary efficacy results.

Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network.

INTRODUCTION: Traditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks to advance diabetes surveillance efforts in youth and young adults through the use of large-volume electronic health record (EHR) data. The network has two primary aims, namely: (1) to refine and validate EHR-based computable phenotype algorithms for accurate identification of type 1 and type 2 diabetes among youth and young adults and (2) to estimate the incidence and prevalence of type 1 and type 2 diabetes among youth and young adults and trends therein. The network aims to augment diabetes surveillance capacity in the USA and assess performance of EHR-based surveillance. This paper describes the DiCAYA Network and how these aims will be achieved. METHODS AND ANALYSIS: The DiCAYA Network is spread across eight geographically diverse US-based centres and a coordinating centre. Three centres conduct diabetes surveillance in youth aged 0-17 years only (component A), three centres conduct surveillance in young adults aged 18-44 years only (component B) and two centres conduct surveillance in components A and B. The network will assess the validity of computable phenotype definitions to determine diabetes status and type based on sensitivity, specificity, positive predictive value and negative predictive value of the phenotypes against the gold standard of manually abstracted medical charts. Prevalence and incidence rates will be presented as unadjusted estimates and as race/ethnicity, sex and age-adjusted estimates using Poisson regression. ETHICS AND DISSEMINATION: The DiCAYA Network is well positioned to advance diabetes surveillance methods. The network will disseminate EHR-based surveillance methodology that can be broadly adopted and will report diabetes prevalence and incidence for key demographic subgroups of youth and young adults in a large set of regions across the USA.

Electronic health records identify timely trends in childhood mental health conditions.

BACKGROUND: Electronic health records (EHRs) data provide an opportunity to collect patient information rapidly, efficiently and at scale. National collaborative research networks, such as PEDSnet, aggregate EHRs data across institutions, enabling rapid identification of pediatric disease cohorts and generating new knowledge for medical conditions. To date, aggregation of EHR data has had limited applications in advancing our understanding of mental health (MH) conditions, in part due to the limited research in clinical informatics, necessary for the translation of EHR data to child mental health research. METHODS: In this cohort study, a comprehensive EHR-based typology was developed by an interdisciplinary team, with expertise in informatics and child and adolescent psychiatry, to query aggregated, standardized EHR data for the full spectrum of MH conditions (disorders/symptoms and exposure to adverse childhood experiences (ACEs), across 13 years (2010-2023), from 9 PEDSnet centers. Patients with and without MH disorders/symptoms (without ACEs), were compared by age, gender, race/ethnicity, insurance, and chronic physical conditions. Patients with ACEs alone were compared with those that also had MH disorders/symptoms. Prevalence estimates for patients with 1+ disorder/symptoms and for specific disorders/symptoms and exposure to ACEs were calculated, as well as risk for developing MH disorder/symptoms. RESULTS: The EHR study data set included 7,852,081 patients < 21 years of age, of which 52.1% were male. Of this group, 1,552,726 (19.8%), without exposure to ACEs, had a lifetime MH disorders/symptoms, 56.5% being male. Annual prevalence estimates of MH disorders/symptoms (without exposure to ACEs) rose from 10.6% to 2010 to 15.1% in 2023, a 44% relative increase, peaking to 15.4% in 2019, prior to the Covid-19 pandemic. MH categories with the largest increases between 2010 and 2023 were exposure to ACEs (1.7, 95% CI 1.6-1.8), anxiety disorders (2.8, 95% CI 2.8-2.9), eating/feeding disorders (2.1, 95% CI 2.1-2.2), gender dysphoria/sexual dysfunction (43.6, 95% CI 35.8-53.0), and intentional self-harm/suicidality (3.3, 95% CI 3.2-3.5). White youths had the highest rates in most categories, except for disruptive behavior disorders, elimination disorders, psychotic disorders, and standalone symptoms which Black youths had higher rates. Median age of detection was 8.1 years (IQR 3.5-13.5) with all standalone symptoms recorded earlier than the corresponding MH disorder categories. CONCLUSIONS: These results support EHRs' capability in capturing the full spectrum of MH disorders/symptoms and exposure to ACEs, identifying the proportion of patients and groups at risk, and detecting trends throughout a 13-year period that included the Covid-19 pandemic. Standardized EHR data, which capture MH conditions is critical for health systems to examine past and current trends for future surveillance. Our publicly available EHR-mental health typology codes can be used in other studies to further advance research in this area.

A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program.

As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.

The real world experience of pediatric primary hyperoxaluria patients in the PEDSnet clinical research network.

The rarity of primary hyperoxaluria (PH) challenges our understanding of the disease. The purpose of our study was to describe the course of clinical care in a United States cohort of PH pediatric patients, highlighting health service utilization. We performed a retrospective cohort study of PH patients < 18 years old in the PEDSnet clinical research network from 2009 to 2021. Outcomes queried included diagnostic imaging and testing related to known organ involvement of PH, surgical and medical interventions specific to PH-related renal disease, and select PH-related hospital service utilization. Outcomes were evaluated relative to cohort entrance date (CED), defined as date of first PH-related diagnostic code. Thirty-three patients were identified: 23 with PH type 1; 4 with PH type 2; 6 with PH type 3. Median age at CED was 5.0 years (IQR 1.4, 9.3 years) with the majority being non-Hispanic white (73%) males (70%). Median follow-up between CED and most recent encounter was 5.1 years (IQR 1.2, 6.8). Nephrology and Urology were the most common specialties involved in care, with low utilization of other sub-specialties (12%-36%). Most patients (82%) had diagnostic imaging used to evaluate kidney stones; 11 (33%) had studies of extra-renal involvement. Stone surgery was performed in 15 (46%) patients. Four patients (12%) required dialysis, begun in all prior to CED; four patients required renal or renal/liver transplant.    Conclusion: In this large cohort of U.S. PH children, patients required heavy health care utilization with room for improvement in involving multi-disciplinary specialists. What is Known: • Primary hyperoxaluria (PH) is rare with significant implications on patient health. Typical involvement includes the kidneys; however, extra-renal manifestations occur. • Most large population studies describe clinical manifestations and involve registries. What is New: • We report the clinical journey, particularly related to diagnostic studies, interventions, multispecialty involvement, and hospital utilization, of a large cohort of PH pediatric patients in the PEDSnet clinical research network. • There are missed opportunities, particularly in that of specialty care, that could help in the diagnosis, treatment, and even prevention of known clinical manifestations.

Safety of intravenous lacosamide in hospitalized children and neonates.

OBJECTIVE: Seizures are common in critically ill children and neonates, and these patients would benefit from intravenous (IV) antiseizure medications with few adverse effects. We aimed to assess the safety profile of IV lacosamide (LCM) among children and neonates. METHODS: This retrospective multicenter cohort study examined the safety of IV LCM use in 686 children and 28 neonates who received care between January 2009 and February 2020. RESULTS: Adverse events (AEs) were attributed to LCM in only 1.5% (10 of 686) of children, including rash (n = 3, .4%), somnolence (n = 2, .3%), and bradycardia, prolonged QT interval, pancreatitis, vomiting, and nystagmus (n = 1, .1% each). There were no AEs attributed to LCM in the neonates. Across all 714 pediatric patients, treatment-emergent AEs occurring in >1% of patients included rash, bradycardia, somnolence, tachycardia, vomiting, feeling agitated, cardiac arrest, tachyarrhythmia, low blood pressure, hypertension, decreased appetite, diarrhea, delirium, and gait disturbance. There were no reports of PR interval prolongation or severe cutaneous adverse reactions. When comparing children who received a recommended versus a higher than recommended initial dose of IV LCM, there was a twofold increase in the risk of rash in the higher dose cohort (adjusted incidence rate ratio = 2.11, 95% confidence interval = 1.02-4.38). SIGNIFICANCE: This large observational study provides novel evidence demonstrating the tolerability of IV LCM in children and neonates.

Skeletal Outcomes in Children and Young Adults with Glomerular Disease.

BACKGROUND: Children with glomerular disease have unique risk factors for compromised bone health. Studies addressing skeletal complications in this population are lacking. METHODS: This retrospective cohort study utilized data from PEDSnet, a national network of pediatric health systems with standardized electronic health record data for more than 6.5 million patients from 2009 to 2021. Incidence rates (per 10,000 person-years) of fracture, slipped capital femoral epiphysis (SCFE), and avascular necrosis/osteonecrosis (AVN) in 4598 children and young adults with glomerular disease were compared with those among 553,624 general pediatric patients using Poisson regression analysis. The glomerular disease cohort was identified using a published computable phenotype. Inclusion criteria for the general pediatric cohort were two or more primary care visits 1 year or more apart between 1 and 21 years of age, one visit or more every 18 months if followed >3 years, and no chronic progressive conditions defined by the Pediatric Medical Complexity Algorithm. Fracture, SCFE, and AVN were identified using SNOMED-CT diagnosis codes; fracture required an associated x-ray or splinting/casting procedure within 48 hours. RESULTS: We found a higher risk of fracture for the glomerular disease cohort compared with the general pediatric cohort in girls only (incidence rate ratio [IRR], 1.6; 95% CI, 1.3 to 1.9). Hip/femur and vertebral fracture risk were increased in the glomerular disease cohort: adjusted IRR was 2.2 (95% CI, 1.3 to 3.7) and 5 (95% CI, 3.2 to 7.6), respectively. For SCFE, the adjusted IRR was 3.4 (95% CI, 1.9 to 5.9). For AVN, the adjusted IRR was 56.2 (95% CI, 40.7 to 77.5). CONCLUSIONS: Children and young adults with glomerular disease have significantly higher burden of skeletal complications than the general pediatric population.

Diagnostic Code-Based Screening for Identifying Children with Primary Hyperoxaluria.

PURPOSE: We evaluated the utility of diagnostic codes to screen for patients with primary hyperoxaluria (PH) and evaluate their positive predictive value (PPV) in identifying children with this rare condition in PEDSnet, a clinical research network of pediatric health systems that shares electronic health records data. MATERIALS AND METHODS: We conducted a cross-sectional study of children who received care at 7 PEDSnet institutions from January 2009 through January 2021. We developed and applied screening criteria using diagnostic codes that generated 3 categories of the hypothesized probability of PH. Tier 1 had specific diagnostic codes for PH; tier 2 had codes for hyperoxaluria, oxalate nephropathy, or oxalosis; and tier 3 had a combination of ≥2 codes for disorder of carbohydrate metabolism and ≥1 code for kidney stones. We reviewed the electronic health records of patients with possible PH to confirm PH diagnosis and evaluate the accuracy and timing of diagnostic codes. The PPV of the codes was compared across tiers, time, PH type, and site. RESULTS: We identified 341 patients in the screen; 33 had confirmed PH (9.7%). Tier 1 had the highest proportion of PH; however, the PPV was only 20%. The degree to which an institution accurately represented point of care diagnoses in the data extraction process was predictive of higher PPV. The PPV of diagnostic codes was highest for PH3 (100%) and lowest for PH1 (22.8%). CONCLUSIONS: Diagnostic codes for PH have poor PPV. Findings suggest that one should be careful in research using large databases in which source validation is not possible.

A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER program.

Background: As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. Methods and Findings: In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS-CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. Conclusions: The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses. Funding Source: This research was funded by the National Institutes of Health (NIH) Agreement OT2HL161847-01 as part of the Researching COVID to Enhance Recovery (RECOVER) program of research. Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the RECOVER Program, the NIH or other funders.

Testing an Automated Approach to Identify Variation in Outcomes among Children with Type 1 Diabetes across Multiple Sites.

Introduction: Efficient methods to obtain and benchmark national data are needed to improve comparative quality assessment for children with type 1 diabetes (T1D). PCORnet is a network of clinical data research networks whose infrastructure includes standardization to a Common Data Model (CDM) incorporating electronic health record (EHR)-derived data across multiple clinical institutions. The study aimed to determine the feasibility of the automated use of EHR data to assess comparative quality for T1D. Methods: In two PCORnet networks, PEDSnet and OneFlorida, the study assessed measures of glycemic control, diabetic ketoacidosis admissions, and clinic visits in 2016-2018 among youth 0-20 years of age. The study team developed measure EHR-based specifications, identified institution-specific rates using data stored in the CDM, and assessed agreement with manual chart review. Results: Among 9,740 youth with T1D across 12 institutions, one quarter (26%) had two or more measures of A1c greater than 9% annually (min 5%, max 47%). The median A1c was 8.5% (min site 7.9, max site 10.2). Overall, 4% were hospitalized for diabetic ketoacidosis (min 2%, max 8%). The predictive value of the PCORnet CDM was >75% for all measures and >90% for three measures. Conclusions: Using EHR-derived data to assess comparative quality for T1D is a valid, efficient, and reliable data collection tool for measuring T1D care and outcomes. Wide variations across institutions were observed, and even the best-performing institutions often failed to achieve the American Diabetes Association HbA1C goals (<7.5%).

Testing the Use of Data Drawn from the Electronic Health Record to Compare Quality.

INTRODUCTION: Health systems spend $1.5 billion annually reporting data on quality, but efficacy and utility for benchmarking are limited due, in part, to limitations of data sources. Our objective was to implement and evaluate measures of pediatric quality for three conditions using electronic health record (EHR)-derived data. METHODS: PCORnet networks standardized EHR-derived data to a common data model. In 13 health systems from 2 networks for 2015, we implemented the National Quality Forum measures: % children with sickle cell anemia who received a transcranial Doppler; % children on antipsychotics who had metabolic screening; and % pediatric acute otitis media with amoxicillin prescribed. Manual chart review assessed measure accuracy. RESULTS: Only 39% (N = 2,923) of 7,278 children on antipsychotics received metabolic screening (range: 20%-54%). If the measure indicated screening was performed, the chart agreed 88% of the time [95% confidence interval (CI): 81%-94%]; if it indicated screening was not done, the chart agreed 86% (95% CI: 78%-93%). Only 69% (N = 793) of 1,144 children received transcranial Doppler screening (range across sites: 49%-88%). If the measure indicated screening was performed, the chart agreed 98% of the time (95% CI: 94%-100%); if it indicated screening was not performed, the chart agreed 89% (95% CI: 82%-95%). For acute otitis media, chart review identified many qualifying cases missed by the National Quality Forum measure, which excluded a common diagnostic code. CONCLUSIONS: Measures of healthcare quality developed using EHR-derived data were valid and identified wide variation among network sites. This data can facilitate the identification and spread of best practices.

Human-centered development of an electronic health record-embedded, interactive information visualization in the emergency department using fast healthcare interoperability resources.

OBJECTIVE: Develop and evaluate an interactive information visualization embedded within the electronic health record (EHR) by following human-centered design (HCD) processes and leveraging modern health information exchange standards. MATERIALS AND METHODS: We applied an HCD process to develop a Fast Healthcare Interoperability Resources (FHIR) application that displays a patient's asthma history to clinicians in a pediatric emergency department. We performed a preimplementation comparative system evaluation to measure time on task, number of screens, information retrieval accuracy, cognitive load, user satisfaction, and perceived utility and usefulness. Application usage and system functionality were assessed using application logs and a postimplementation survey of end users. RESULTS: Usability testing of the Asthma Timeline Application demonstrated a statistically significant reduction in time on task (P < .001), number of screens (P < .001), and cognitive load (P < .001) for clinicians when compared to base EHR functionality. Postimplementation evaluation demonstrated reliable functionality and high user satisfaction. DISCUSSION: Following HCD processes to develop an application in the context of clinical operations/quality improvement is feasible. Our work also highlights the potential benefits and challenges associated with using internationally recognized data exchange standards as currently implemented. CONCLUSION: Compared to standard EHR functionality, our visualization increased clinician efficiency when reviewing the charts of pediatric asthma patients. Application development efforts in an operational context should leverage existing health information exchange standards, such as FHIR, and evidence-based mixed methods approaches.

Are there birth cohort effects in disparities in child obesity by maternal education?

BACKGROUND: Children belonging to the same birth cohort (i.e., born in the same year) experience shared exposure to a common obesity-related milieu during the critical early years of development-e.g., secular beliefs and feeding practices, adverse chemical exposures, food access and nutrition assistance policies-that set the stage for a shared trajectory of obesity as they mature. Fundamental cause theory suggests that inequitable distribution of recent efforts to stem the rise in child obesity may exacerbate cohort-based disparities over time. METHODS: Data were from electronic health records spanning 2007-2016 linked to birth records for children ages 2-19 years. We used hierarchical age-period-cohort models to investigate cohort effects on disparities in obesity related to maternal education. We hypothesized that maternal education-based disparities in prevalence of obesity would be larger among more recent birth cohorts. RESULTS: Sex-stratified models adjusted for race/ethnicity showed substantial obesity disparities by maternal education that were evident even at young ages: prevalence among children with maternal education < high school compared to maternal college degree was approximately three times as high among girls and twice as high among boys. For maternal education < high school, disparities compared to maternal college degree were higher in more recent birth cohorts. Among girls, this disparity cohort effect was evident at younger ages (at age 4, the disparity increased by 4 [0.1-8] percentage points per 5 birth years), while among boys it was larger at older ages (at age 16, the disparity increased by 7 [1-14] percentage points per 5 birth years). CONCLUSIONS: There may be widening maternal education-based disparities in child obesity by birth cohort at some ages.

Reducing electronic health record-related burnout in providers through a personalized efficiency improvement program.

OBJECTIVE: To give providers a better understanding of how to use the electronic health record (EHR), improve efficiency, and reduce burnout. MATERIALS AND METHODS: All ambulatory providers were offered at least 1 one-on-one session with an "optimizer" focusing on filling gaps in EHR knowledge and lack of customization. Success was measured using pre- and post-surveys that consisted of validated tools and homegrown questions. Only participants who returned both surveys were included in our calculations. RESULTS: Out of 1155 eligible providers, 1010 participated in optimization sessions. Pre-survey return rate was 90% (1034/1155) and post-survey was 54% (541/1010). 451 participants completed both surveys. After completing their optimization sessions, respondents reported a 26% improvement in mean knowledge of EHR functionality (P < .01), a 19% increase in the mean efficiency in the EHR (P < .01), and a 17% decrease in mean after-hours EHR usage (P < .01). Of the 401 providers asked to rate their burnout, 32% reported feelings of burnout in the pre-survey compared to 23% in the post-survey (P < .01). Providers were also likely to recommend colleagues participate in the program, with a Net Promoter Score of 41. DISCUSSION: It is possible to improve provider efficiency and feelings of burnout with a personalized optimization program. We ascribe these improvements to the one-on-one nature of our program which provides both training as well as addressing the feeling of isolation many providers feel after implementation. CONCLUSION: It is possible to reduce burnout in ambulatory providers with personalized retraining designed to improve efficiency and knowledge of the EHR.

Standardizing Clinical Diagnoses: Evaluating Alternate Terminology Selection.

In most electronic health record (EHR) systems, clinicians record diagnoses using interface terminologies, such as Intelligent Medical Objects (IMO). When extracting data from EHRs for collaborative research, local codes are often transformed to standard terminologies for consistent analyses despite the potential for loss of fidelity. EHR diagnosis codes may be standardized directly during the Extract-Transform-Load (ETL) process to the "Meaningful Use" clinical data exchange standard, SNOMED-CT, or to the International Classification of Diseases (ICD) terminologies commonly used for billing. We examined the performance of ETL standardization via the direct IMO mapping to SNOMED-CT, and via IMO mapping to ICD-9-CM or ICD-10-CM followed by UMLS mapping to SNOMED-CT. We found that for both ICD-9-CM and ICD-10-CM, only 24-27% of diagnosis codes map to the same SNOMED-CT code selected by the direct IMO-SNOMED crosswalk. We identified that differences in mapping lead to loss in the granularity and laterality of the initial diagnosis.

Use of EHR-Based Pediatric Quality Measures: Views of Health System Leaders and Parents.

Measures of health care quality are produced from a variety of data sources, but often, physicians do not believe these measures reflect the quality of provided care. The aim was to assess the value to health system leaders (HSLs) and parents of benchmarking on health care quality measures using data mined from the electronic health record (EHR). Using in-context interviews with HSLs and parents, the authors investigated what new decisions and actions benchmarking using data mined from the EHR may enable and how benchmarking information should be presented to be most informative. Results demonstrate that although parents may have little experience using data on health care quality for decision making, they affirmed its potential value. HSLs expressed the need for high-confidence, validated metrics. They also perceived barriers to achieving meaningful metrics but recognized that mining data directly from the EHR could overcome those barriers. Parents and HSLs need high-confidence health care quality data to support decision making.

Design and Refinement of a Data Quality Assessment Workflow for a Large Pediatric Research Network.

BACKGROUND: Clinical data research networks (CDRNs) aggregate electronic health record data from multiple hospitals to enable large-scale research. A critical operation toward building a CDRN is conducting continual evaluations to optimize data quality. The key challenges include determining the assessment coverage on big datasets, handling data variability over time, and facilitating communication with data teams. This study presents the evolution of a systematic workflow for data quality assessment in CDRNs. IMPLEMENTATION: Using a specific CDRN as use case, the workflow was iteratively developed and packaged into a toolkit. The resultant toolkit comprises 685 data quality checks to identify any data quality issues, procedures to reconciliate with a history of known issues, and a contemporary GitHub-based reporting mechanism for organized tracking. RESULTS: During the first two years of network development, the toolkit assisted in discovering over 800 data characteristics and resolving over 1400 programming errors. Longitudinal analysis indicated that the variability in time to resolution (15day mean, 24day IQR) is due to the underlying cause of the issue, perceived importance of the domain, and the complexity of assessment. CONCLUSIONS: In the absence of a formalized data quality framework, CDRNs continue to face challenges in data management and query fulfillment. The proposed data quality toolkit was empirically validated on a particular network, and is publicly available for other networks. While the toolkit is user-friendly and effective, the usage statistics indicated that the data quality process is very time-intensive and sufficient resources should be dedicated for investigating problems and optimizing data for research.

A Bayesian latent class approach for EHR-based phenotyping.

Phenotyping, ie, identification of patients possessing a characteristic of interest, is a fundamental task for research conducted using electronic health records. However, challenges to this task include imperfect sensitivity and specificity of clinical codes and inconsistent availability of more detailed data such as laboratory test results. Despite these challenges, most existing electronic health records-derived phenotypes are rule-based, consisting of a series of Boolean arguments informed by expert knowledge of the disease of interest and its coding. The objective of this paper is to introduce a Bayesian latent phenotyping approach that accounts for imperfect data elements and missing not at random missingness patterns that can be used when no gold-standard data are available. We conducted simulation studies to compare alternative phenotyping methods under different patterns of missingness and applied these approaches to a cohort of 68 265 children at elevated risk for type 2 diabetes mellitus (T2DM). In simulation studies, the latent class approach had similar sensitivity to a rule-based approach (95.9% vs 91.9%) while substantially improving specificity (99.7% vs 90.8%). In the PEDSnet cohort, we found that biomarkers and clinical codes were strongly associated with latent T2DM status. The latent T2DM class was also strongly predictive of missingness in biomarkers. Glucose was missing in 83.4% of patients (odds ratio for latent T2DM status = 0.52) while hemoglobin A1c was missing in 91.2% (odds ratio for latent T2DM status = 0.03 ), suggesting missing not at random missingness. The latent phenotype approach may substantially improve on rule-based phenotyping.

Influence of simulation on electronic health record use patterns among pediatric residents.

Objective: Electronic health record (EHR) simulation with realistic test patients has improved recognition of safety concerns in test environments. We assessed if simulation affects EHR use patterns in real clinical settings. Materials and Methods: We created a 1-hour educational intervention of a simulated admission for pediatric interns. Data visualization and information retrieval tools were introduced to facilitate recognition of the patient's clinical status. Using EHR audit logs, we assessed the frequency with which these tools were accessed by residents prior to simulation exposure (intervention group, pre-simulation), after simulation exposure (intervention group, post-simulation), and among residents who never participated in simulation (control group). Results: From July 2015 to February 2017, 57 pediatric residents participated in a simulation and 82 did not. Residents were more likely to use the data visualization tool after simulation (73% in post-simulation weeks vs 47% of combined pre-simulation and control weeks, P <. 0001) as well as the information retrieval tool (85% vs 36%, P < .0001). After adjusting for residents' experiences measured in previously completed inpatient weeks of service, simulation remained a significant predictor of using the data visualization (OR 2.8, CI: 2.1-3.9) and information retrieval tools (OR 3.0, CI: 2.0-4.5). Tool use did not decrease in interrupted time-series analysis over a median of 19 (IQR: 8-32) weeks of post-simulation follow-up. Discussion: Simulation was associated with persistent changes to EHR use patterns among pediatric residents. Conclusion: EHR simulation is an effective educational method that can change participants' use patterns in real clinical settings.